In [1]:
#################
### SIR Model ###
#################
import scipy.integrate as spi
import numpy as np
import pylab as pl
beta = 0.1934 # 논문에 나온 beta 값 참조 (2020.02.10 중국논문발표)
# 우리나라도 중국과 마찬가지로 2차 지역감염이 시작되고 전염력이 2/18 당시 매우 높았던것으로 보여 논문의 beta 도입.
gamma = 1/14 # I ->R 회복율 = 평균 회복기간의 역수
t_inc = 1.0
t_end = 150.0
# 2/18 31번확진자 나온날 기준으로 초기 SIR 모델 만듦.
S0 = int(input('Input the accumulated number of tests: '))
I0 = int(input('Input the accumulated number of positive results: '))
R0 = int(input('Input the accumulated number of releases: '))
# S0 = 9772 ; I0 = 31; R0 = 12
N = S0 + I0 + R0
S0 = S0 /N # susceptible hosts
I0 = I0 /N # infectious hosts
R0 = R0 /N # recovered hosts
Input = (S0, I0, 0.0)
Input
def simple_SIR(INT, t):
'''The main set of equation'''
Y=np.zeros((3))
X = INT # S0, I0
Y[0] = -beta * X[0] * X[1]
Y[1] = beta*X[0]*X[1] - gamma * X[1]
Y[2] = gamma * X[1]
return Y # for spicy.odeint
t_start =0.0 ;
t_range = np.arange(t_start, t_end + t_inc, t_inc)
SIR= spi.odeint(simple_SIR, Input, t_range)
pl.figure(figsize=(15,8))
pl.plot(SIR[:, 0], '-g', label='Susceptibles')
pl.plot(SIR[:, 2], '-k', label='Recovereds')
pl.plot(SIR[:, 1], '-r', label='Infectious')
pl.legend(loc=0)
pl.title('Prediction of Simple nCOV-19 SIR model')
pl.xlabel('Time(day)')
pl.ylabel('individuals')
pl.show()
Input the accumulated number of tests: 9772 Input the accumulated number of positive results: 31 Input the accumulated number of releases: 12
